Improving Data Management and Decision Support Systems in Agriculture (Burleigh Dodds Series in Agricultural Science): 85

✍ Scribed by Leisa Armstrong

• Publisher: Burleigh Dodds Science Publishing Limited
• Year: 2020
• Tongue: English
• Leaves: 341
• Edition: Illustrated
• Category: Library

✦ Synopsis

This collection reviews and summarises the wealth of research on key challenges in developing better data management and decision support systems (DSS) for farmers and examples of how those systems are being deployed to optimise efficiency in crop and livestock production.

Part 1 reviews general issues underpinning effective decision support systems (DSS) such as data access, standards, tagging and security. Part 2 contains case studies of the practical application of data management and DSS in areas such as crop planting, nutrition and use of rotations, livestock feed and pasture management as well as optimising supply chains for fresh produce.

With its distinguished editor and international team of authors, Improving data management and decision support systems in agriculture will be a standard reference for researchers in agriculture and computer science interested in improving data management, modelling and decision support systems in farming, as well as government and other agencies supporting the use of precision farming techniques, and companies supplying decision support services to the farming sector.

✦ Table of Contents

Improving data management and decision support systems in agriculture
Contents
Series list
Introduction
Part 1 General issues
Chapter 1 Improving data access for more effective decision making in agriculture
1 Introduction
2 Key issues in current availability of data
3 Use of data for decision making: case studies
4 Current trends
5 Conclusions
6 Where to look for further information
7 References
Chapter 2 Improving data standards and integration for more effective decision-making in agriculture
1 Introduction
2 Business process modelling to identify data requirements
3 Data flows for a particular process: the example of variable rate fertilization
4 Linking platforms and software
5 Creating a reference architecture for interoperability, replicability and reuse
6 Key elements in data management
7 Conclusions
8 Where to look for further information
9 References
Chapter 3 Improving data identification and tagging for more effective decision making in agriculture
1 Introduction
2 Structuring the data
3 Case study: plant phenotyping
4 Conclusion and future trends
5 Where to look for further information
6 Acknowledgements
7 References
Chapter 4 Advances in data security for more effective decision-making in agriculture
1 Introduction
2 Security challenges in PA systems
3 System architecture and legal recourse
4 Security framework considerations for PA systems
5 Modern cyberattack methods
6 Classifying cyberattack source psychology
7 Cybersecurity frameworks for PA
8 Case study: PA system assessment
9 Future trends
10 Conclusion
11 Where to look for further information
12 References
13 Appendix
Chapter 5 Advances in artificial intelligence (AI) for more effective decision making in agriculture
1 Introduction
2 Agricultural DSS using AI technologies: an overview
3 Data and image acquisition
4 Core AI technologies
5 Case study 1: AgData DSS tool for western Australian broad acre cropping
6 Case study 2: GeoSense
7 Case study 3: Rice-based DSS
8 Summary and future trends
9 Where to look for information
10 References
Chapter 6 Improving data management and decision-making in precision agriculture
1 Introduction
2 Remote sensing technologies
3 Geographic information system (GIS) technologies
4 Sensors and sensor networks
5 Statistical and crop simulation models
6 Identifying variability in crop production systems
7 Summary and future trends
8 Where to look for further information
9 References
Part 2 Case studies
Chapter 7 Decision support systems (DSS) for better fertiliser management
1 Introduction
2 Direct methods for determining crop nitrogen requirements for decision support
3 Indirect methods for determining crop nitrogen requirements for decision support: simulation models
4 Indirect methods for determining crop nitrogen requirements for decision support: yield forecasts using data-driven approaches
5 Indirect methods for determining crop nitrogen requirements for decision support: yield forecasts based on water supply
6 Decision support in action: case studies
7 Case study 1: nitrogen fertiliser applications using a data-driven approach
8 Case study 2: nitrogen fertiliser decision-making based on soil moisture predictions
9 Comparing the two approaches
10 Conclusion and future trends
11 References
Chapter 8 Developing decision-support systems for crop rotations
1 Introduction
2 Key information challenges
3 Ecological theory
4 Agronomic models
5 Encoding farmer decisions
6 Design principles
7 Outlook
8 Where to look for further information
9 References
Chapter 9 Decision-support systems for pest monitoring and management
1 Introduction
2 Pest identification
3 Pest monitoring
4 Pest forecasting
5 Integrated pest management (IPM)
6 Case studies
7 Summary and future trends
8 Where to look for further information
9 References
Chapter 10 Developing decision support systems for improving data management in agricultural supply chains
1 Introduction
2 Decisions in supporting data management
3 Decision tools
4 Principal case studies
5 Conclusion and future trends
6 References
Chapter 11 Developing decision support systems for optimizing livestock diets in farms
1 Introduction
2 Mathematical programming models for livestock production: a review
3 Linear programming (LP) models to minimize feed costs: solutions and sensitivity analysis
4 Goal programming (GP) models: balancing costs and environmental impact
5 Decision support systems and data management for sustainable diets
6 Case study 1: sustainable rations for intensive broiler production
7 Case study 2: reducing emissions in pig production
8 Summary and future trends in research
9 Acknowledgements
10 Where to look for further information
11 References
Chapter 12 Developing decision-support systems for pasture and rangeland management
1 Introduction
2 Decision-support systems (DSSs) in pasture and rangeland management
3 Decision-making processes of pasture and rangeland farmers
4 Development of effective decision-support tools
5 Case studies of decision-support system (DSS) development in pasture and rangeland management
6 Conclusion and future trends
7 Where to look for further information
8 References
Index