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Plant diseases affect the quality and quantity of agricultural products and have an impact on food safety. These effects result in a loss of income in the production sectors which are particularly critical for developing countries. Visual inspection by subject matter experts is time-consuming, expensive and not scalable for large farms. As such, the automation of plant disease detection is a feasible solution to prevent losses in yield. Nowadays, one of the most popular approaches for this automation is to use drones. Though there are several articles published on the use of drones for plant disease detection, a systematic overview of these studies is lacking. To address this problem, a systematic literature review (SLR) on the use of drones for plant disease detection was undertaken and 38 primary studies were selected to answer research questions related to disease types, drone categories, stakeholders, machine learning tasks, data, techniques to support decision-making, agricultural product types and challenges. It was shown that the most common disease is blight; fungus is the most important pathogen and grape and watermelon are the most studied crops. The most used drone type is the quadcopter and the most applied machine learning task is classification. Color-infrared (CIR) images are the most preferred data used and field images are the main focus. The machine learning algorithm applied most is convolutional neural network (CNN). In addition, the challenges to pave the way for further research were provided.

This paper presents a conceptual framework that reflects the current state of thinking on tokenizing circularity in agri-food systems. The framework is built upon classifications of tokens and the key principles of circular economy and shows how tokenization can support circularity in agri-food systems through the flows of information and flows of value. Based on an integrative review of literature on tokenization, blockchain and the circular economy and multiple case studies in the agri-food domain, we show the relevance of tokenization to the circular economy in three ways: 1) enhancing traceability of physical and digital objects in supply chains; 2) improving transparency and credibility of circularity claims; 3) facilitating collaborative business ecosystems with incentives for more circular production and distribution. Based on the framework, we derive important research questions for future research agenda on tokenizing circularity in agri-food systems.

PurposeTraceability of food is of paramount importance to the increasingly sustainability-conscious consumers. Several tracking and tracing systems have been developed in the AgriFood sector in order to prove to the consumers the origins and processing of food products. Critical challenges in realizing food's traceability include cooperating with multiple actors on common data sharing standards and data models.Design/methodology/approachThis research applies a design science approach to showcase traceability that includes preharvest activities and conditions in a case study. The authors demonstrate how existing data sharing standards can be applied in combination with new data models suitable for capturing transparency information about plant production.FindingsTogether with existing studies on farm-to-fork transparency, our results demonstrate how to realize transparency from field to fork and enable producers to show a complete bill of sustainability.Originality/valueThe existing standards and data models address transparency challenges in AgriFood chains from the moment of harvest up to retail (farm-to-fork) relatively well, but not what happens before harvest. In order to address sustainability concerns, there is a need to collect data about production activities related to product quality and sustainability before harvesting and share it downstream the supply chain. The ability to gather data on sustainability practices such as reducing pesticide, herbicide, fertilizer and water use are crucial requirements for producers to market their produce as quality and sustainable products.

One of the key elements of precision agriculture is the farm management information system (FMIS) that is responsible for data management, analytics and subsequent decision support. Various FMISs have been developed to support the management of farm businesses. A key artefact in the development of FMISs is the software architecture that defines the gross level structure of the system. The software architecture is important for understanding the system, analysing the design decisions and guiding the further development of the system based on the architecture. To assist in the design of the FMIS architecture, several reference architectures have been provided in the literature. Unfortunately, in practice, it is less trivial to derive the application architecture from these reference architectures. Two underlying reasons for this were identified. First of all, it appears that the proposed reference architectures do not specifically focus on FMIS but have a rather broad scope of the agricultural domain in general. Secondly, the proposed reference architectures do not seem to have followed the proper architecture documentation guidelines as defined in the software architecture community, lack precision, and thus impeding the design of the required application architectures. Presented in this article is a novel reference architecture that is dedicated to the specific FMIS domain, and which is documented using the software architecture documentation guidelines. In addition, the systematic approach for deriving application architectures from the proposed reference architecture is provided. To illustrate the approach, the results of multi-case study research are shown in which the presented reference architecture is used for deriving different FMIS application architectures.

Nowadays, we produce and consume a large volume of products giving insufficient regard to what happens to the products before, during and after use or consumption. Globally roughly one-third of food produced is lost or wasted each year. As the world population and per-capita income increase, the problem gets worse. This lifestyle based on “linear” economy has become untenable, and therefore the adoption of a system of business processes that implement circular economy principles approach is proposed. However, it is not clear to what extent the current practice of documenting business processes in supply chains, the SCOR (Supply Chain Operations Reference) model, reflects and captures the circular economy practices. In this study, we reviewed the state-of-the-art literature to identify to what extent the SCOR model has been used in connection to circular economy and to what extent SCOR supports “circular” business processes.

Nowadays, different types of farm management information systems (FMIS) are being used in practice in several sectors of farming, such as dairy, arable, fruits, vegetables, and meat farming. The goal of this research is to identify, evaluate, and synthesize existing FMISs in the Dutch dairy sector and present the state-of-the-art. We performed a multivocal literature review (MLR) to find sources both in scientific and grey literature. A grey literature search was adopted because most of the FMISs were not reported in the scientific literature. To support and improve the effectiveness of the MLR process, an online survey was first sent to Dutch dairy farmers to identify the FMISs that are being used in practice. With the help of the MLR process, we identified 50 FMISs used by Dutch dairy farmers. We identified 33 features of these FMISs and listed the advantages and disadvantages of the FMISs. Copyright: 2022 by the authors. Licensee MDPI, Basel, Switzerland. Funding: Authors thanks to the Molde University College-Specialized Univ. in Logistics, Norway for the support of Open access fund. Scopus 2-s2.0-85122665807

Applications of data analytics, and recently machine learning, in pig farming have been investigated in literature and the results indicate great potential for data-driven decision support at various scales of the sector—from farm to the management of entire supply chains. However, there is insufficient overview of the studies conducted so far. Particularly, there is little insight into the extent of studies conducted in the context of actual business cases. In this study we conducted a systematic literature review to shed light on the state-of-the-art knowledge about data-driven decision making in the pig sector. In order to cover both classical data analysis techniques and machine learning, we used two separate search strings to search the literature. The results show that the various attributes of live pigs and slaughter data are used in analytics. Most studies focus on the occurrence and prevention of diseases, followed by DNA-related analysis and the effect of feeding strategies on growth. Among the studies we analysed, there was a large variation in herd size under study. Most studies used a selected group of pigs in an experimental environment; fewer studies used a larger number of pigs. Notably, all studies except two focussed on real-life business contexts where real-time data is used. The application of machine learning, mainly the use of random forest and neural network algorithms, took off since 2018. Current studies focus on isolated and one-off problems, and we suggest future research to consider the complexity encountered in real-life business circumstances and routine decision making through the integration of data analytics within farm information management systems.

Despite increasing global environmental concerns, we continue to consume large amounts of products with little regard to what happens before, during, and after their use. Roughly one-third of the food produced is wasted. Because the world’s population is expected to grow to 10 billion by 2050, adopting circular economy practices will become essential. The transition towards a circular economy requires adopting business processes that support circular economy practices across supply chains. Currently, the SCOR (Supply Chain Operations Reference) model is the most widely used, and widely known, approach for studying and evaluating supply chain business processes. It is, however, unclear to what extent circular principles are included in the SCOR model. Past studies indicating missing processes for circular supply chain management in the SCOR model have made limited efforts in capturing the current state-of-the-practice. We conducted an online survey of 60 companies engaged in 14 different types of economic activities to study the SCOR level 2 business processes adopted in practice. In addition to the 22 level 2 business processes documented in SCOR, we identified six additional level 2 circular business processes that the respondents recognized as being commonly applied within their businesses. The results clearly show that the current SCOR model does not fully represent circular business processes in the state-of-the-practice.

This paper proposes a drone policy framework for developing drone regulations for Africa, expediting the individual countries’ developing drone regulations. This research was conducted following concerns raised by individuals and businesses whose imported drones were seized by customs or had to go through a cumbersome process for approval to use drones for lack of regulations. To derive the policy framework, we matched the provisions of 15 existing drone regulations from Africa to the International Civil Aviation Organization (ICAO) Manual on Remotely Piloted Aircraft Systems (RPAS) using a decomposition modelling technique. The proposed framework is presented as a formal business process model and is evaluated in a multi-case study approach in two different case studies. The results indicate that two key aspects/elements are crucial in the development of a drone framework and thus are included in our framework: (i) stakeholders’ inclusion in the policy development and (ii) regulators liaising with security agencies like the police and immigration officials to implement and enforce developed regulations. Applying the proposed framework in describing existing regulations reveals that 40-85% of provisions in existing regulations conform to the provisions in ICAO’s RPAS manual.

Global food demands have led to the rapid introduction of Information Communication Technology (ICT) innovations in the agriculture sector—one such innovation is drone technology. Drones are used in precision agriculture, including aerial observation, sensing, and the spraying of pesticides. Regulations on the use of drones are necessary because drones can violate privacy rules, data protection rights, and public peace. However, many African countries have either very restrictive regulations, or no proper regulation in place, making the process of acquiring a license for drone operation cumbersome. In this study, we present the results of a literature review that explores the current drone regulations in Sub-Saharan Africa and the results of a systematic literature review (SLR) and survey study whereby we have interviewed the relevant stakeholders, in order to understand the challenges posed by the regulations to the effective use of drones for agriculture. The results indicate that the regulations contain about 40 to 85 per cent of the provisions of the International Civil Aviation Organization (ICAO) manual on Remotely Piloted Aircraft Systems (RPASs). In addition, whilst the SLR focused on the technology, safety, ethics and regulatory hurdles towards drones, the interviewees focused on the need for skill and awareness among the responsible authorities to enforce regulations, and the need for sustainability and participatory process in defining regulations.

In recent years, several researchers and practitioners applied machine learning algorithms in the dairy farm context and discussed several solutions to predict various variables of interest, most of which were related to incipient diseases. The objective of this article is to identify, assess, and synthesize the papers that discuss the application of machine learning in the dairy farm management context. Using a systematic literature review (SLR) protocol, we retrieved 427 papers, of which 38 papers were determined as primary studies and thus were analysed in detail. More than half of the papers (55 %) addressed disease detection. The other two categories of problems addressed were milk production and milk quality. Seventy-one independent variables were identified and grouped into seven categories. The two prominent categories that were used in more than half of the papers were milking parameters and milk properties. The other categories of independent variables were milk content, pregnancy/calving information, cow characteristics, lactation, and farm characteristics. Twenty-three algorithms were identified, which we grouped into four categories. Decision tree-based algorithms are by far the most used followed by artificial neural network-based algorithms. Regression-based algorithms and other algorithms that do not belong to the previous categories were used in 13 papers. Twenty-three evaluation parameters were identified of which 7 were used 3 or more times. The three evaluation parameters that were used by more than half of the papers are sensitivity, specificity, RMSE. The challenges most encountered were feature selection and unbalanced data and together with problem size, overfitting/estimating, and parameter tuning account for three-quarters of the challenges identified. To the best of our knowledge, this is the first SLR study on the use of machine learning to improve dairy farm management, and to this end, this study will be valuable not only for researchers but also practitioners in dairy farms.

Various Farm Management Information Systems (FMISs) have been developed to support the management of the farm businesses. These FMISs typically support the different domains of the agricultural sector, such as arable and dairy farming; and include different set of features, such as crop, field, and financial management. These FMISs also have to deal with diverse obstacles during their development and adoption, such as lack of standardized data, cost and usability. Though several papers have been published in the past several years on this topic, there has been no explicit attempt to systematically review these papers to identify and characterize the features and obstacles. The objective of this study is to identify and describe the state-of-the-art of FMISs and as such pave the way for further research and development of FMISs. We applied a systematic literature review protocol in which we included the literature published from 2008 to 2018. We found 1048 papers of which 38 papers were selected as primary studies that we analyzed further in detail. From the detailed analysis, we identified 81 unique FMIS features and 51 unique obstacles of FMISs. We have systematically ranked the identified features and obstacles and describe the key associated aspects. These aspects include the agricultural domains, modeling approaches, delivery models, and identified stakeholders.

Ghana produces 20% of global cocoa output and is the second-largest producer and exporter of cocoa beans in the world. The Ghana cocoa industry is, however, challenged by a lack of adequate decision support systems across the supply chain. Particularly, cocoa farmers have limited access to information, which impedes planning, pricing, benchmarking, and quality management. In order to address this asymmetric access to information and ensure fair access to information that will allow the making of informed decisions, the supply chain stakeholders need to adapt their business processes. For identifying the requirements for better information flow, we identified the existing (as-is) processes through a systematic survey study in Ghana. We then identified the main problems and bottlenecks, designed new (to-be) business processes, and showed how IT systems support and enable inclusive business models in the Ghana cocoa industry. To enable inclusiveness, we incorporated IT solutions that improve information flows towards cocoa farmers. The results show that there are many opportunities (e.g., improving farmer livelihoods and a potential increase in export earnings) in the cocoa sector for Ghana and all stakeholders that can be utilized when there is chain-wide collaboration, equitable access to services, and proper use of IT systems.

Context Businesses today must collaborate in a coordinated fashion. To collaborate, they must align their business processes and IT by complying to a common reference architecture. The common reference architecture that addresses their specific collaboration requirements is generally an adaptation of an existing generic reference architecture. However, a design framework for adapting reference architectures is lacking. Objective In this paper we propose a design framework for aligning business processes and IT across diverse collaborating organisations in order to derive a more specific reference architecture from a generic one. Method We developed the design framework using the guidelines of ISO/IEC/IEEE standard for modelling design viewpoints and validated it in a real-life business case study. Results We developed an architectural design framework which we call BITA* that is composed of three coherent architectural design viewpoints. The BP2BP alignment viewpoint provides alignment modelling abstractions for business analysts to be used to align business collaboration processes. The IT2IT alignment viewpoint provides alignment modelling abstractions for software architects to be used to align distributed IT systems. The BP2IT alignment viewpoint provides alignment modelling abstractions for interdisciplinary teams of business and IT specialists for aligning the mapping of business collaboration processes and the underlying distributed IT. The modelling abstractions are applied in a case study to derive a reference architecture for meat supply chain transparency systems. Conclusion A key challenge in developing the design framework is the difficulty of comparing models of business processes and IT that come from diverse organisations. Our main contribution is the set of modelling abstractions, which enabled us to represent business processes and IT in a uniform and comparable manner, and the systematic approach for applying the modelling abstractions. The framework is applied in the agri-food sector and needs to be evaluated further in multiple case studies from various application domains.

The poultry meat supply chain is complex and therefore vulnerable to many potential contaminations that may occur. To ensure a safe product for the consumer, an efficient traceability system is required that enables a quick and efficient identification of the potential sources of contamination and proper implementation of mitigation actions. In this study, we explored the use of graph theory to construct a food supply chain network for the broiler meat supply chain in the Netherlands and tested it as a traceability system. To build the graph, we first identified the main actors in the supply chain such as broiler breeder farms, broiler farms, slaughterhouses, processors, and retailers. The capacity data of each supply chain actor, represented by its production or trade volumes, were gathered from various sources. The trade relationships between the supply chain actors were collected and the missing relationships were estimated using the gravity model. Once the network was modeled, we computed degree centrality and betweenness centrality to identify critical nodes in the network. In addition, we computed trade density to get insight into the complexity of sub-networks. We identified the critical nodes at each stage of the Dutch broiler meat supply chain and verified our results with a domain expert of the Dutch poultry industry and literature. The results showed that processors with own slaughtering facility were the most critical points in the broiler meat supply chain.

Machine learning is an important decision support tool for crop yield prediction, including supporting decisions on what crops to grow and what to do during the growing season of the crops. Several machine learning algorithms have been applied to support crop yield prediction research. In this study, we performed a Systematic Literature Review (SLR) to extract and synthesize the algorithms and features that have been used in crop yield prediction studies. Based on our search criteria, we retrieved 567 relevant studies from six electronic databases, of which we have selected 50 studies for further analysis using inclusion and exclusion criteria. We investigated these selected studies carefully, analyzed the methods and features used, and provided suggestions for further research. According to our analysis, the most used features are temperature, rainfall, and soil type, and the most applied algorithm is Artificial Neural Networks in these models. After this observation based on the analysis of machine learning-based 50 papers, we performed an additional search in electronic databases to identify deep learning-based studies, reached 30 deep learning-based papers, and extracted the applied deep learning algorithms. According to this additional analysis, Convolutional Neural Networks (CNN) is the most widely used deep learning algorithm in these studies, and the other widely used deep learning algorithms are Long-Short Term Memory (LSTM) and Deep Neural Networks (DNN).

Over the past several years, farm enterprises have grown in size substantially while their number has steadily declined. As the size of their farms grow more and more farmers are deploying information systems, commonly called as Farm Management Information Systems (FMIS), to manage the day to day activities of their farms. The deployment of FMIS enable farmers to capture detailed data that can potentially be analysed by data mining tools to provide valuable information for optimizing the farm enterprises. However, data mining is generally not a common feature of many FMIS. In order to evaluate the suitability of data mining for use in FMIS, we performed two case studies using data captured in FMIS and applying data mining. Microsoft Azure Machine Learning Studio is chosen because it provides a simple drag-and-drop visual interface that can be used by farm domain experts. We addressed two common problems in dairy farming: calving prediction of dairy cows and prediction of lactation value of milking cows. In both cases we built data mining models and run experiments and our results in both cases indicate that the required data is available from FMIS and data mining techniques provides acceptable performance. We also showed that farm domain experts can easily use a user-friendly and drag-and-drop data mining tools with minimal initial training. Based on the insight from the two case studies and literature study, we identified several decision problems that can be addressed with data mining such as heat prediction and lameness prediction.

BACKGROUND: Continuity of care is important for child well-being in all settings where postnatal retention of mother-infant pairs in care remains a challenge. This analysis reports on completeness of patient-held infant Road to Health Booklets (RtHBs), amongst HIV exposed and unexposed infants during the first two years after the RtHB was launched country-wide in South Africa. METHODS: Secondary data were analysed from two nationally representative, cross-sectional surveys, conducted in 2011-12 and 2012-13. These surveys aimed to measure early effectiveness of the national programme for preventing vertical HIV transmission. Participants were eligible for this analysis if they were 4-8 weeks old, receiving their six-week immunisation, not needing emergency care and had their RtHBs reviewed. Caregivers were interviewed and data abstracted from RtHBs. RtHB completeness across both surveys was defined as the proportion of RtHBs with any of the following indicators recorded: infant birth weight, BCG immunisation, maternal syphilis results and maternal HIV status. A partial proportional odds logistic regression model was used to identify factors associated with completeness. Survey sampling weights were included in all analyses. RESULTS: Data from 10?415 (99.6%) participants in 2011-12 and 9529 (99.2%) in 2012-13 were analysed. Overall, recording of all four indicators increased from 23.1% (95% confidence interval (CI) ?=?22.2-24.0) in 2011-12 to 43.3% (95% CI?=?42.3-44.4) in 2012-13. In multivariable models, expected RtHB completeness (ie, recording all four indicators vs recording of

Display Omitted We mould the concept Software Ecosystems to the agricultural domain.We propose a reference architecture for Farm Software Ecosystems.Our reference architecture describes an organizational and technical infrastructure.We motivate that our reference architecture can improve farm enterprise integration.Our reference architecture is used to review some existing initiatives. Smart farming is a management style that includes smart monitoring, planning and control of agricultural processes. This management style requires the use of a wide variety of software and hardware systems from multiple vendors. Adoption of smart farming is hampered because of a poor interoperability and data exchange between ICT components hindering integration. Software Ecosystems is a recent emerging concept in software engineering that addresses these integration challenges. Currently, several Software Ecosystems for farming are emerging. To guide and accelerate these developments, this paper provides a reference architecture for Farm Software Ecosystems. This reference architecture should be used to map, assess design and implement Farm Software Ecosystems. A key feature of this architecture is a particular configuration approach to connect ICT components developed by multiple vendors in a meaningful, feasible and coherent way. The reference architecture is evaluated by verification of the design with the requirements and by mapping two existing Farm Software Ecosystems using the Farm Software Ecosystem Reference Architecture. This mapping showed that the reference architecture provides insight into Farm Software Ecosystems as it can describe similarities and differences. A main conclusion is that the two existing Farm Software Ecosystems can improve configuration of different ICT components. Future research is needed to enhance configuration in Farm Software Ecosystems.

When multiple organisations want to collaborate with one another they have to integrate their business processes. This requires aligning the collaborative business processes and the underlying IT (Information Technology). Realizing the required alignment is, however, not trivial and is the subject of this thesis. We approached the issue of alignment in three steps. First, we explored business-IT alignment problems in detail in a real-life business case. This is done in order to clarify what alignment of business processes and IT systems across a collaboration network entails. Second, we provided a business-IT alignment framework called BITA* (pronounce bita-star). The framework provides modelling abstractions for alignment. Third, we applied the framework in two real-life case studies, including the real-life business case used in step one. By applying the framework in practice we showed that the framework can, in fact, help to address the business-IT alignment problems that we identified in the first step. The work presented in this thesis is conducted over a number of years in the context of four large EU sponsored research projects. The projects focused on alignment problems in two very distinct application areas. Two projects were about realizing transparency systems for meat supply chains and constitute the first case study. The other two projects were about realizing multidisciplinary modelling collaboration systems and constitute the second case study. Although the projects were conducted sequentially the research questions were addressed iteratively over the years. The research methodology that shows how the framework is designed and how the case studies are applied is discussed in detail in chapter 2. In chapter 3 we present BITA*, a Business-IT Alignment framework for multiple collaborating organisations. The main challenges in designing BITA* have been what models to consider for alignment and how to compare them in order to make explicit statements about alignment. We addressed this problem by introducing allocation and alignment modelling constructs to help the alignment process, and the concept of business collaboration model to represent the models that have to be aligned. We identified three groups of stakeholders for whom we designed explicit design viewpoints and associated allocation and alignment models. The Business Process to Business Process (BP2BP) alignment viewpoint is designed for business analysts who have to align diverse business collaboration process models. The IT to IT (IT2IT) alignment viewpoint is designed for software architects to align the distribution of data and IT systems across a collaboration network. The Business Process to IT (BP2IT) alignment viewpoint is designed for an interdisciplinary team of business analysts and software architects who have to align the different ways of supporting business collaboration processes with distributed IT system. An essential element of this thesis has been elaborating how business-IT alignment problems occur in the context of multi-organisational collaboration. The case studies were used to demonstrate business-IT alignment concerns. Particularly, the details of the first case study presented in chapters 4 and 5 were used in chapter 3 to help derive the alignment framework. The case study presented an ideal problem scenario since realizing transparency across supply chains is intrinsically a collaborative effort. The second case study was used to enhance the validity of our approach. The results of the second case study are presented in chapter 6. The alignment framework was designed during the iterative process we followed when realizing a generic transparency system for meat supply chains. To realize the required generic transparency system we needed a reference architecture. To derive the reference architecture we adapted an already existing and broadly-accepted generic reference architecture. We have to adapt the generic reference architecture in order to address specific requirements of the meat sector that were not considered in the generic reference architecture. The adaptation process made it clear that we needed models for representing business collaborations. We, therefore, introduced the notion of business collaboration model, which we used both to model reference architectures and to adapt them. Adaptation required aligning the generic reference architecture with the diverse business collaboration models adopted by the organisations that have to collaborate. The alignment framework is thus used for adapting a generic reference architecture in order to create a reference architecture that the collaborating organisations can, and are willing to, adopt. We identified three types of business collaboration models: business collaboration process model, business collaboration IT model, and a model for representing the relationship between these two. A business collaboration process model is a business process model that spans a collaboration network. A business collaboration IT model is a model of the distribution of the IT across the collaboration network. A business collaboration process-IT model is a model of the relationships between the elements of the business collaboration processes and the elements of the distributed IT. Each organisation is considered to adopt its own business collaboration models. For instance, different actors in meat supply chains have different views on how chain-wide transparency should be realized. Which business processes and IT systems each organisation has to deploy and use depends on the business collaboration models each food operator adopts. If two different food operators adopt the same set of business collaboration models, they are aligned; otherwise they are misaligned. Hence, alignment entails comparing the different business collaboration models adopted by the participating organisations. The results of the alignment process are explicit statements about how convergent or divergent the organisations are from the chosen generic reference architecture. The explicit statements of alignment guide how best the generic and the corresponding organisational business collaboration models can be adapted to create a better state of alignment. To further enhance the validity of the overall approach the second case study was conducted. The second case study was a retrospective investigation of two past research projects focusing on aligning environmental modelling processes and IT systems. A retrospective case study was chosen because launching a new business-IT alignment project involving multiple collaborating organisations was not feasible. The projects were undertaken to support the European Water Framework Directive, which mandated, among other things, participatory, multidisciplinary, river-basin wide and model-based studies to manage the water resources of Europe. The directive particularly required a collaborative approach to building environmental decision support systems and to deriving methodologies for applying existing decision support systems. We applied BITA* to aligning environmental modelling processes and IT systems in order to evaluate the suitability of the framework to addressing alignment problems in other application areas. The contributions of the thesis are summarized in chapter 7. The contributions include a number of design artefacts, which can be grouped into four categories: constructs, models, methods, and instantiations. The contribution in the first category includes the conceptualization of allocation and alignment. The contributions in the second category include allocation and alignment models, and reference architectures. Allocation models are representations of business collaboration models in a form that can be compared and are the basis for alignment modelling. The main contribution in the third category is the BITA* systematic approach to alignment modelling. The contributions in the fourth category are the software systems developed with the help of the reference architectures.

Organizations very often need to collaborate to achieve their business goals. Hereby it is important that the collaboration concerns are properly identified and reflected in their businesses. In practice, the business process design and architecture design are often carried out separately. This often leads to a misalignment between the business process and architecture design, a problem which becomes more severe when multiple collaborating organizations are involved. To address this problem, it is important to provide the proper design abstractions that can be used to detect and correct misalignments. To this end, we propose the architecture collaboration viewpoint that can be used by teams of business analysts and software architects when addressing business collaboration concerns. The collaboration viewpoint uses elements from business process and architecture viewpoints to provide new modeling artifacts for alignment. The design artefacts are mapping tables and workflow pattern diagrams that are used to identify misalignments and redesign the business processes. The viewpoint facilitates the communication between business analysts and architects. We illustrate the collaboration viewpoint for a food supply chain transparency system from a real industrial case study.

Despite the perceived value of DSS in informing environmental and natural resource management, DSS tools often fail to be adopted by intended end users. By drawing together the experience of a global group of EDSS developers, we have identified and assessed key challenges in EDSS development and offer recommendations to resolve them. Challenges related to engaging end users in EDSS development emphasise the need for a participatory process that embraces end users and stakeholders throughout the design and development process. Adoption challenges concerned with individual and organisational capacities to use EDSS and the match between EDSS and organisational goals can be overcome through the use of an internal champion to promote the EDSS at different levels of a target organisation; co-ordinate and build capacity within the organisation, and; ensure that developers maintain focus on developing EDSS which are relatively easy and inexpensive to use and update (and which are perceived as such by the target users). Significant challenges exist in relation to ensuring EDSS longevity and financial sustainability. Such business challenges may be met through planning and design that considers the long-term costs of training, support, and maintenance; revenue generation and licensing by instituting processes which support communication and interactions; and by employing software technology which enables easy model expansion and re use to gain an economy of scale and reduce development costs. A final group of perhaps more problematic challenges relate to how the success of EDSS ought to be evaluated. Whilst success can be framed relatively easily in terms of interactions with end users, difficulties of definition and measurability emerge in relation to the extent to which EDSS achieve intended outcomes. To tackle the challenges described, the authors provide a set of best practice recommendations concerned with promoting design for ease of use, design for usefulness, establishing trust and credibility, promoting EDSS acceptance, and starting simple and small in functionality terms. Following these recommendations should enhance the achievement of successful EDSS adoption, but more importantly, help facilitate the achievement of desirable social and environmental outcomes.

Businesses today rarely operate in isolation but must collaborate with others in a coordinated fashion. To address collaboration concerns, business analysts need to design business processes. Business process designs have a direct impact on the required software systems and the corresponding architectural design. Conversely, the architectural design imposes constraints on the business process designs. Unfortunately, business processes and software architectures are often designed separately leading to a misalignment between the two. To bridge this gap we propose the architecture collaboration viewpoint to be used by teams of business analysts and software architects when addressing business collaboration concerns. The collaboration viewpoint uses elements from business process and architecture viewpoints to provide new modeling artifacts for alignment. The design artefacts are mapping tables and workflow pattern diagrams that are used to identify misalignments and redesign the busine ss processes. The viewpoint facilitates the communication between business analysts and architects. We illustrate the collaboration viewpoint for a food supply chain transparency system from a real industrial case study.

Quality assurance in model based water management is needed because of some frequently perceived shortcomings, e.g. a lack of mutual understanding between modelling team members, malpractice and a tendency of modellers to oversell model capabilities. Initiatives to support quality assurance focus on single domains and often follow a textbook approach with guidelines and checklists. A modelling process involves a complex set of activities executed by a team. To manage this complex, usually multidisciplinary process, to guide users through it and enhance the reproducibility of modelling work a software product has been developed, aiming at supporting the full modelling process by offering an ontological knowledge base (KB) and a Modelling Support Tool (MoST). The KB consists of a generic part for modelling, but also parts specific for various water management domains, for different types of users and for different levels of modelling complexity. MoST's guiding component filters relevant knowledge from the KB depending on the user profile and needs. Furthermore, MoST supports different types of users by monitoring what they actually do and by producing customized reports for diverse audiences. In this way MoST facilitates co-operation in teams, modelling project audits and re-use of experiences of previous modelling projects.

Quality assurance (QA) is defined as protocols and guidelines to support the proper application of models. In the water management context we classify QA guidelines according to how much focus is put on the dialogue between the modeller and the water manager as: (Type 1) Internal technical guidelines developed and used internally by the modeller's organisation; (Type 2) Public technical guidelines developed in a public consensus building process; and (Type 3) Public interactive guidelines developed as public guidelines to promote and regulate the interaction between the modeller and the water manager throughout the modelling process. State-of-the-art QA practices vary considerably between different modelling domains and countries. It is suggested that these differences can be explained by the scientific maturity of the underlying discipline and differences in modelling markets in terms of volume of jobs outsourced and level of competition. The structure and key aspects of new generic guidelines and a set of electronically based supporting tools that are under development within the HarmoniQuA project are presented. Model credibility can be enhanced by a proper modeller-manager dialogue, rigorous validation tests against independent data, uncertainty assessments, and peer reviews of a model at various stages throughout its development.

Includes abstract., Includes bibliographical references., This study assesses the UN Inter-agency Group for Child Mortality Estimation (IGME) methods of estimating the infant and neonatal mortality rates from the under-five mortality rates in countries affected by HIV/AIDS. It uses Botswana, Malawi and South Africa as case studies. The assessment is made by comparing the IGME results with estimates from projection models and empirical results computed from survey data and vital statistics data corrected for the level of incompleteness for the countries included in the study. In addition, relevant literature is reviewed in order to determine the reasonableness and impact (on the results produced) of the assumptions made by the method.

Today's consumers increasingly demand products that are produced sustainably and ethically. As a result, businesses need to address sustainability and social responsibility issues and find a proper balance between people, planet and profit (PPP) aspects of their production chains. Software tools can play an important role in mapping out the current state of PPP aspects along the production chain, and in the design and evaluation of improvement options. There are indeed many tools that are claimed to be useful for sustainability and social responsibility considerations. Yet, a tool that addresses all three aspects of value creation holistically and facilitates discussion is missing. In this paper we present the development of such a tool, called QChain. The development of this tool was based on ideas from soft systems methodology and inputs from a multidisciplinary team of experts and managers. The tool is intended to support group discussions, particularly during the early stages of innovation processes aimed at improving PPP aspects of production chains. It enables users to visualize the essential elements of the current production chain showing the current PPP values, and explore and compare possible future production chain scenarios and the corresponding PPP values. QChain's visualization helps discussants get a rich appreciation of the current and future scenarios, while the semi-quantitative 'what-if' analysis and scenario comparison enables them to hold objective discussions.