Digitalisation in a local food system:Emphasis on Finnish Lapland

The positive adoption of digital technology within the food sector can boost sustainable development in Finnish Lapland. There is a need for a food system in the region to respond to current trends from consumers and ensure a better supply of local foods that are processed efficiently with minimal waste. In this article, a review of the literature on the benefits of digitalisation as a tool amongst food processors was carried out. The opportunities offered by digital technology are expected to make local food business operators more transparent, efficient and sustainable. Digitalisation can help to minimise the environmental impacts of food processing and ultimately improve sustainability. In meeting the demand of local consumers, distributed and localised manufacturing will help to add value to local food crops, lower transportation and storage costs. The adoption of food digitalisation will open up market accessibility for the locally produced food products in local communities. In the future, digitalisation is likely to have major impacts in the local food system of the Lapland region

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

낙농업에서 정보 기술 적용의 결정 요인에 관한 연구: 탐색적 고찰

학위논문 (박사)-- 서울대학교 대학원 : 농경제사회학부(지역정보전공), 2012. 8. 최영찬.낙농 경영의 정보 관리는 가축 관리, 높은 품질의 제품에 대한 소비자의 수요, 그리고 정부 규제 등에 관한 지식의 증가와 정보 시스템 강화 때문에 더 복잡하다. 정밀 농업이라고 불리는 (Wang et al., 2006), 효과적인 유효한 실시간 데이터를 얻는 것을 (Zhang et al., 2002) 과학기술은 가능하게 한다. 정밀 축산업은 가축 관리를 지원하는 정보기술 사용의 증가 (Banhazi et al., 2007Mertens et al., 2011) 와 낙농 경영 활동으로부터 유래된 비교적 새로운 학문이다. 그러나 농산물에 대한 정보 기술 적용에 대한 연구는 거의 이뤄지고 있지 않다 (Thomas and Callahan, 2002). 농부들은 1980년대와 1990년대를 거치는 동안 정보 기술을 거의 이용하지 못했다 (Schmidt et al., 1994). 또한 농부들은 정보 기술을 도입하여 적용하는 비율이 낮았다 (Morris et al., 1995). 뉴질랜드의 연구들은 낙농 농장이 그들의 유제품 생산에 혜택을 줄 새로운 기술을 도입하는 것이 늦거나 하지 않는 것을 보여준다 (Crawford et al., 1989Deane, 1993Edwards and Parker, 1994Stantiall and Parker, 1997). 일반적으로 기업들도 정보 기술 적용과 역량을 충분히 활용하지 않는다 (Jasperson et al., 2005). 사용자들은 일반적으로 기술의 특징적인 부분만 낮은 수준에서 이용하며, 기술이 제공하는 더 많은 확장된 다양한 부분들은 거의 사용하지 않는다 (Davenport, 1998Lyytinen and Hirschheim, 1987Mabert et al., 2001Osterland, 2000Rigby et al., 2002and Ross and Weill 2002). 제 2 장에서 정밀 농업과 정밀 축산업에 관한 채택, 잠재적 기능과 적용에 대한 문헌들을 보여주고 있으며, 낙농 경영의 자동화된 시스템에 대해서도 검토했다. 이 연구의 목적은 크게 두 가지이다. 첫째는 한국의 낙농 경영 정보 시스템의 후(後) 채택을 위한 요인을 설명하고 식별하는 것이다. 두 번째 목표는 한국 낙농 경영 정보 시스템의 동화(기술 적용)에 대한 요인을 조사하는 것이다. 3 장과 4 장에서는 이 연구의 대상인 후(後) 채택과 동화(기술 적용)에 초점을 맞추고 있다. 제 3장에서, 첫 번째 연구인 한국에서 낙농 경영 정보 시스템의 후(後) 채택을 실시한 경험적 사례가 논의된다. 초기 기술 이용자들 기술을 채택하는데 끼친 영향에 대해 알아보기 위해서 사례 연구를 발전시켰다. 개별 기술 이용자들과 환경적, 기술적, 그리고 조직적 요소들이 연구 되었다. 연구의 결과는 낙농 경영 정보 시스템과 농업 정보 시스템의 도입이 한국과 다른 지역에서 늦어지는 원인과 좀 더 나은 전망을 제시한다. 이미 이러한 시스템을 시행하고 있는 낙농업의 농장의 매니저를 대상으로 한 현장 인터뷰를 통해 수집된 질적 데이터를 사용하여 평가되었고, 농부의 개별적인 특성보다는 환경 조건이 더 관련성이 크다라는 것을 보여준다. 이에 득실을 따지는 것(신뢰 대 경제) 보다는 오히려 기술이 좋은것이라는 일반적인 생각들이 있었다. 농부들이 기술을 경영에 도입하지만, 그들은 여전히 수동으로 농장에서 상황들을 관찰하려는 것을 선호한다. 대부분의 농장 과정에서 이러한 것은 다소 요령으로 남아있으며, 농부들은 전통적인 관행들을 따라 하는 것을 선호한다. 이러한 관계는 이러한 새로운 기술을 도입하는데 기여하거나 방해가 된다. 사례의 경우 기술 – 조직 – 환경 프레임 워크 (Tornatsky and Fleisher, 1990)를 따르거나 깊게 관련되는 것을 보여줬다. 이러한 결과는 제 4장으로 이어질 일반적인 프레임 워크와 일련의 가설들이다. 결과는 16개의 가설들 중에서 11개를 증명했다. 이 연구는 한국의 낙농 경영 정보 시스템을 조사한 최초의 실험적인 다양한 방법론을 사용한 사례연구이다. 이 연구는 낙농 매니저와 공급 업체 지원의 관계에 대한 더 나은 이해를 제공한다. 제 4장에서 이러한 기술의 적용에 영향을 미치는 요인들과 확장된 낙농 경영 정보 시스템의 사용에 대해 알아보고 있다. 제 4 장에서는 두 번째 주제인, 낙농 경영 정보 시스템을 적용하는 과정을 알아보는 양적 연구이다. 이러한 낙농 경영 정보의 적용 과정은 기술-조직-환경 프레임워크를 통해 조사되고, 과정 자동화의 수준과 확장된 사용에 의해 제시된 동화 단계를 통해 진행된다. 이론적 모델은 두 가지의 동화과정과 확장된 사용 단계를 통해 진행된다. 이러한 단계들은 농장 운영 활동들, 즉 과정 자동화의 수준에 따라 나뉜다. 정보 기술의 채택과 관련된 많은 기존의 연구들이 있지만 농업과 낙농의 맥락으로부터 정보 기술의 흡수(동화)에 대한 연구는 거의 없었다. 연구에서 리커트 타입의 설문(a Likert-type survey)을 통해 얻어진 자료를 활용했다. 가설을 확인하기 위해서 확인적 요인 분석과 최소 제곱 법(PLS: partial least square)을 하였으며, 결론적으로 일상 업무에 대한 측정은 과정 자동화의 수준에 큰 긍정적인 영향이 있으며, 이 효과는 낙농 산업의 경험과 연령에 부정적인 영향이 있다는 것을 보여준다. 또한 농장 크기가 가축 건강 관리를 자동화 하는 시스템 사용을 용이 하게하는 것으로 나타났으며, 농부들과 외부 조직 사람들이 앞으로의 사용에 영향을 미칠 수 있으며, 시스템은 외부와의 관계와 농장의 이미지를 향상 할 수 있는 것으로 나타났다. 일상 업무나 생산 계획, 그리고 가축들의 건강 관리와 같은 일들이 농장 운영에서 시스템의 적용과정과 확장된 시스템의 사용을 촉진한다. 이 연구는 정보 시스템 프레임 워크를 소개하고, 이론적인 관점에서 확장된 농장 업무에도 적용 가능함을 보여준다. 연구의 결과는 또한 낙농장 환경에서 가축들의 생물학적 시기와 관련된 새로운 요인들을 제시하며 이러한 요인들은 정보 기술의 채택이나 동화 과정 연구에서 잘 보여지지 않는 것들이다. 주제어: 동화, 낙농 경영 정보 시스템, 확장된 사용, 사후 채택, 정밀 농업, 정밀 축산업, 기술-조직-환경(TOE) 체제Information management in the dairy industry is more complex because of the intensification of information systems and the increase in knowledge about animal management, consumer demand for higher quality products, and government regulations. Technology enables precision agriculture (Wang et al., 2006), which obtains effective data in real time (Zhang et al., 2002). Precision livestock farming originates from the increased use of information technology for livestock and dairy management activities (Banhazi et al., 2007Mertens et al., 2011). However, studies indicate that the application of information technology in agricultural production is minimal (Thomas and Callahan, 2002). Farmers did not take advantage of information technology during the 1980s and 1990s (Schmidt et al., 1994). Farmers have shown a low rate of information technology adoption (Morris et al., 1995). Studies in New Zealand indicate that dairy farms have not adopted or have been slow to adopt new technologies that would benefit their milk production (Crawford et al., 1989Stantiall and Parker, 1997). In general, businesses do not utilize the full potential of information technology applications and components (Jasperson et al., 2005). Businesses typically operate at low levels of component use and rarely extend the use of available components that are offered by the technology (Davenport, 1998and Ross and Weill 2002). There are two objectives for this dissertation. The first objective is to investigate factors for the post-adoption of a dairy management information system in South Korea. The second objective is to investigate factors for the assimilation and extended use of a dairy management information system. The first and second objectives are investigated in Chapters 3 and 4. The objectives are applied as two studies that focus on post-adoption and assimilation of information technology used in dairy management. A literature review on precision agriculture and precision livestock farming is also investigated in Chapter 2. Chapter 2 investigates the adoption, potential functions and actual applications of precision agriculture and precision livestock farming. Automated dairy systems are also reviewed. Chapter 3 is an exploratory case study that examines the post-adoption of a dairy management information system in South Korea. We develop a multi-method case study to investigate the influences for adoption by early adopters. Individual adopter and environmental, technological and organizational factors are investigated. The results of this study can provide better insight for why the adoption of a dairy management information system and agricultural information systems in Korea and elsewhere is lagging. The propositions were evaluated using qualitative data collected through on site interviews with dairy managers that have already implemented the system. The study results suggest that environmental conditions appear more relevant than individual characteristics of the farmer. There was a general feeling that technology is a good thing rather than bottom-line profit. Trust is more important than economics. Although farmers adopted the technology, they still prefer to observe conditions on the farm manually. A number of farm processes remained somewhat of an art. Farmers prefer to follow known routines. This relationship may contribute or hinder the adoption of this emerging technology. The results of this case study closely follow and are linked to the Technology-Organization-Environment Framework (Tornatsky and Fleisher, 1990). The results of the study were a set of propositions and general framework, which lead to Chapter 4. We were able to support eleven of sixteen propositions. This is the first exploratory, multi-method case study to look at a dairy management information system in South Korea. The study further provides a better understanding of the relationship between dairy managers and vendor support. We investigate factors that affect assimilation and extended use of a dairy management information system in Chapter 4. Chapter 4 is a quantitative study that examines the assimilation and extended use of an information system used in dairy management. We initially investigate this study through the Technology-Organization-Environment Framework. The theoretical model proceeds through two assimilation and extended use stages. The first stage is farm operational activities. These farm operational activities are daily operations, production planning and herd health management. The second stage is the level of process automation. There are many studies that are concerned with the adoption of information technology. There have rarely been studies on assimilation of information technology from an agricultural and dairy context. The study utilizes data collected through a Likert-type survey. Exploratory and confirmatory factor analysis and partial least squares for hypothesis testing are performed. Results indicate that measures for daily operations have a significant effect on the level of process automation. This effect is negatively impacted by the years of dairy industry experience. There is also evidence that farm size can facilitate information system assimilation and extended use to automate herd health management. Social influences such as other farmers and other support services outside the organization can affect future use of the system. The system can also improve outside relationships and farm image. These factors facilitate the assimilation and extended use of the system in farm operational activities. The study introduces an information systems framework and demonstrates its applicability to extended farm operational activities from a theoretical perspective. The study also introduces a new component that involves biological phases of a domesticated animal in a dairy farm environment. This biological component is rarely seen in information technology adoption and assimilation research.Chapter 1: Overview 1 1.1 Research Background 1 1.2 Problem Statement 2 1.3 Small and Medium-sized Enterprises 3 1.4 Dissertation Objectives and Research Questions 5 1.5 Organization of the Dissertation 6 Chapter 2: Literature Review 8 2.1 Introduction 8 2.2 The Agricultural Technology Revolution 10 2.3 Precision Agriculture 10 2.3.1 Awareness and Adoption of Precision Agriculture 10 2.3.2 Barriers to Adopt and Automate Precision Agriculture 11 2.3.3 Precision Agriculture Applications 13 2.4 Precision Livestock Farming 15 2.4.1 Information Systems in Dairy Management 15 2.4.2 Potential Functions of Precision Livestock Farming 15 2.4.3 Adoption of Precision Livestock Farming 17 2.4.4 Applications of Precision Livestock Farming 18 2.5 Automatic Agricultural Systems 19 2.6 Automatic Milking Systems 24 2.7 Summary 26 Chapter 3: Factors Affecting Adoption of a Dairy Management Information System: An Exploratory Case Study 27 3.1 Introduction 27 3.1.1 Statement of the Problem 28 3.1.2 Significance of the Study 30 3.1.3 Purpose of the Study 31 3.1.4 Research Question 32 3.2 Literature Review 33 3.2.1 Adoption of Information Technology in Agriculture 33 3.2.2 Adoption of Information Technology in Dairy 35 3.2.3 Innovation Diffusion Theory 36 3.3 Theoretical Model and Propositions 38 3.3.1 Adopter characteristics 40 3.3.2 Environment Context 44 3.3.3 Technology Context 46 3.3.4 Organization Context 49 3.4 Methodology 52 3.4.1 Case Study Research Methodology 52 3.4.2 Ethics of Survey Research 56 3.4.3 Study and Interview Permission 57 3.4.4 Research Method 57 3.4.5 Survey Population 59 3.4.6 Sampling Method 59 3.4.7 Sample Validity and Representative Sample 60 3.4.8 Survey Instrument 60 3.4.9 The Dairy Management Information System 61 3.5 Analysis 62 3.5.1 Farm Size 63 3.5.2 Experience 63 3.5.3 Age 64 3.5.4 Education 65 3.5.5 Social Influences 65 3.5.6 Sponsorship 66 3.5.7 Information Sharing 67 3.5.8 Dealer Trust 68 3.5.9 Relative Advantage 68 3.5.10 Knowledge 68 3.5.11 Compatibility 69 3.5.12 Planning 69 3.5.13 Complexity 70 3.5.14 Profitability 71 3.5.15 Cash Flow/Financial Resources 72 3.5.16 Risk-taking/Uncertainty 72 3.6 Research Model Results 74 3.7 Discussion 76 3.7.1 Research Question 76 3.7.2 Findings 76 3.8 Study Limitations and Future Research 79 3.8.1 Theoretical Contributions 81 3.8.2 Practical Contributions 82 3.9 Conclusions 82 Chapter 4: Factors Affecting Assimilation of a Dairy Management Information System: A Quantitative Study 84 4.1 Introduction 84 4.1.2 Problem Statement 85 4.1.3 Small and Medium-sized Enterprises 85 4.1.4 Significance of the Study 87 4.1.5 Intent of the Study and Research Questions 87 4.1.6 Limitations 89 4.1.7 Delimitations 90 4.1.8 Assumptions 91 4.1.9 Organization of the Study 91 4.2 Theoretical Background 92 4.2.1 Adoption-Infusion Process 92 4.2.2 Assimilation Process 93 4.2.3 Technology-Organization-Environment Framework 93 4.2.4 Assimilation 98 4.2.5 Extended Use of Information Technology 100 4.2.6 Dairy Management Activities 102 4.2.7 Dairy Farm Supply Chain 105 4.2.6 Milk Production Cycle 106 4.3 Hypotheses and Model Development 107 4.3.1 Theoretical Model 108 4.3.2 Level of Process Automation 110 4.3.3 Moderator Effects 113 4.3.4 System Complexity 114 4.3.5 System Compatibility 116 4.3.6 Organization Competence 117 4.3.7 Perceived Benefits 119 4.3.8 Social Influences 120 4.3.9 Cooperative Support 121 4.3.10 Control Variables 123 4.3.11 Study Hypotheses 124 4.4 Research Methodology 126 4.4.1 Ethics of Survey Research 126 4.4.2 Study and Survey Permission 127 4.4.3 Research Method 127 4.4.4 Validity of Research Questions and Survey 127 4.4.5 Survey Population 128 4.4.6 Sampling Method 128 4.4.7 Sample Validity 128 4.4.8 Representative Sample 129 4.4.9 The Survey Instrument 129 4.4.10 Dairy Management Information System 131 4.4.11 Operationalization and Validation 132 4.4.12 Descriptive Analysis 134 4.5 Analysis and Results 137 4.5.1 Statistical Tools 137 4.5.2 Measurement Model 139 4.5.3 Hypotheses Testing 144 4.6 Discussion 152 4.6.1 Process Automation 155 4.6.2 Moderator Variables 156 4.6.3 Daily Operations 157 4.6.4 Production Planning 158 4.6.5 Herd Health Management 159 4.6.6 Control Variables 160 4.6.7 Implications 160 4.7 Limitations and Future Research 163 4.8 Conclusions 164 Chapter 5: Conclusions 166 References 171 Appendices 208 Appendix A. List of Abbreviations 208 Appendix B. Case Study Open-Ended Interview Questions 209 Appendix C. Dairy Management Information System Components 209 Appendix D-1. English Survey Questionnaire 211 Appendix D-2. Taiwanese/Chinese Survey Questionnaire 213 Appendix D-3. Korean Survey Questionnaire 215 Appendix E. Farmers Comments 217 Abstract in Korean 219Docto

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