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Improving Data Management and Decision Support Systems in Agriculture [Kietas viršelis]

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  • Formatas: Hardback, 340 pages, aukštis x plotis x storis: 229x152x21 mm, weight: 627 g, Color tables, photos and figures
  • Serija: Burleigh Dodds Series in Agricultural Science 85
  • Išleidimo metai: 28-Apr-2020
  • Leidėjas: Burleigh Dodds Science Publishing Limited
  • ISBN-10: 1786763400
  • ISBN-13: 9781786763402
Kitos knygos pagal šią temą:
Improving Data Management and Decision Support Systems in AgricultureDidesnis paveikslėlis
  • Formatas: Hardback, 340 pages, aukštis x plotis x storis: 229x152x21 mm, weight: 627 g, Color tables, photos and figures
  • Serija: Burleigh Dodds Series in Agricultural Science 85
  • Išleidimo metai: 28-Apr-2020
  • Leidėjas: Burleigh Dodds Science Publishing Limited
  • ISBN-10: 1786763400
  • ISBN-13: 9781786763402
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781786763402.html
Raktažodžiai:
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 DSS in areas such as crop planting and nutrition, livestock feed and pasture management as well as supply chains.

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.
Series list x
Introduction xvi
Part 1 General issues
1 Improving data access for more effective decision making in agriculture
3(14)
Ben Schaap
Suchith Anand
Andre Laperriere
1 Introduction
3(1)
2 Key issues in current availability of data
4(3)
3 Use of data for decision making: case studies
7(3)
4 Current trends
10(2)
5 Conclusions
12(1)
6 Where to look for further information
13(1)
7 References
13(4)
2 Improving data standards and integration for more effective decision-making in agriculture
17(20)
Sjaak Wolfert
1 Introduction
17(2)
2 Business process modelling to identify data requirements
19(1)
3 Data flows for a particular process: the example of variable rate fertilization
20(1)
4 Linking platforms and software
21(4)
5 Creating a reference architecture for interoperability, replicability and reuse
25(2)
6 Key elements in data management
27(6)
7 Conclusions
33(1)
8 Where to look for further information
33(1)
9 References
34(3)
3 Improving data identification and tagging for more effective decision making in agriculture
37(22)
Pascal Neveu
Romain David
Montpellier SupAgro
1 Introduction
37(2)
2 Structuring the data
39(10)
3 Case study: plant phenotyping
49(4)
4 Conclusion and future trends
53(2)
5 Where to look for further information
55(1)
6 Acknowledgements
56(1)
7 References
56(3)
4 Advances in data security for more effective decision-making in agriculture
59(36)
Jason West
1 Introduction
59(3)
2 Security challenges in PA systems
62(6)
3 System architecture and legal recourse
68(2)
4 Security framework considerations for PA systems
70(1)
5 Modern cyberattack methods
71(3)
6 Classifying cyberattack source psychology
74(3)
7 Cybersecurity frameworks for PA
77(2)
8 Case study: PA system assessment
79(3)
9 Future trends
82(1)
10 Conclusion
83(1)
11 Where to look for further information
84(1)
12 References
85(2)
13 Appendix
87(8)
5 Advances in artificial intelligence (AI) for more effective decision making in agriculture
95(40)
L. J. Armstrong
N. Gandhi
P. Taechatanasat
D. A. Diepeveen
1 Introduction
95(1)
2 Agricultural DSS using AI technologies: an overview
96(4)
3 Data and image acquisition
100(2)
4 Core AI technologies
102(7)
5 Case study 1: AgData DSS tool for western Australian broad acre cropping
109(1)
6 Case study 2: GeoSense
110(3)
7 Case study 3: Rice-based DSS
113(3)
8 Summary and future trends
116(1)
9 Where to look for further information
117(3)
10 References
120(15)
6 Improving data management and decision-making in precision agriculture
135(24)
Soumyashree Kar
Rohit Nandan
Rahul Raj
Saurabh Suradhaniwar
J. Adinarayana
1 Introduction
135(1)
2 Remote sensing technologies
136(3)
3 Geographic information system (GIS) technologies
139(1)
4 Sensors and sensor networks
140(2)
5 Statistical and crop simulation models
142(2)
6 Identifying variability in crop production systems
144(2)
7 Summary and future trends
146(1)
8 Where to look for further information
147(1)
9 References
148(11)
Part 2 Case studies
7 Decision support systems (DSS) for better fertiliser management
159(26)
Dhahi Al-Shammari
Patrick Filippi
James P. Moloney
Niranjan S. Wimalathunge
Brett M. Whelan
Thomas F. A. Bishop
1 Introduction
159(2)
2 Direct methods for determining crop nitrogen requirements for decision support
161(2)
3 Indirect methods for determining crop nitrogen requirements for decision support: simulation models
163(2)
4 Indirect methods for determining crop nitrogen requirements for decision support: yield forecasts using data-driven approaches
165(1)
5 Indirect methods for determining crop nitrogen requirements for decision support: yield forecasts based on water supply
166(1)
6 Decision support in action: case studies
167(1)
7 Case study 1: nitrogen fertiliser applications using a data-driven approach
168(5)
8 Case study 2: nitrogen fertiliser decision-making based on soil moisture predictions
173(2)
9 Comparing the two approaches
175(3)
10 Conclusion and future trends
178(1)
11 References
179(6)
8 Developing decision-support systems for crop rotations
185(20)
Zia Mehrabi
1 Introduction
185(2)
2 Key information challenges
187(2)
3 Ecological theory
189(1)
4 Agronomic models
190(3)
5 Encoding farmer decisions
193(1)
6 Design principles
194(3)
7 Outlook
197(1)
8 Whereto look for further information
198(1)
9 References
198(7)
9 Decision-support systems for pest monitoring and management
205(30)
B. Sailaja
Ch. Padmavathi
D. Krishnaveni
G. Katti
D. Subrahmanyam
M. S. Prasad
S. Gayatri
S. R. Voleti
1 Introduction
205(1)
2 Pest identification
206(2)
3 Pest monitoring
208(2)
4 Pest forecasting
210(4)
5 Integrated pest management (IPM)
214(1)
6 Case studies
215(9)
7 Summary and future trends
224(1)
8 Where to look for further information
225(1)
9 References
226(9)
10 Developing decision support systems for improving data management in agricultural supply chains
235(18)
Gerhard Schiefer
1 Introduction
235(3)
2 Decisions in supporting data management
238(3)
3 Decision tools
241(3)
4 Principal case studies
244(5)
5 Conclusion and future trends
249(1)
6 References
250(3)
11 Developing decision support systems for optimizing livestock diets in farms
253(26)
Marina Segura
Conception Maroto
Baldomero Segura
Conception Ginestar
1 Introduction
253(2)
2 Mathematical programming models for livestock production: a review
255(2)
3 Linear programming (LP) models to minimize feed costs: solutions and sensitivity analysis
257(5)
4 Goal programming (GP) models: balancing costs and environmental impact
262(2)
5 Decision support systems and data management for sustainable diets
264(2)
6 Case study 1: sustainable rations for intensive broiler production
266(6)
7 Case study 2: reducing emissions in pig production
272(1)
8 Summary and future trends
273(1)
9 Acknowledgements
274(1)
10 Where to look for further information
275(1)
11 References
275(4)
12 Developing decision-support systems for pasture and rangeland management
279(32)
Callum Eastwood
Brian Dela Rue
1 Introduction
279(1)
2 Decision-support systems (DSSs) in pasture and rangeland management
280(1)
3 Decision-making processes of pasture and rangeland farmers
281(3)
4 Development of effective decision-support tools
284(8)
5 Case studies of decision-support system (DSS) development in pasture and rangeland management
292(10)
6 Conclusion and future trends
302(1)
7 Where to look for further information
303(1)
8 References
304(7)
Index 311
Dr Leisa Armstrong is Senior Lecturer in Computer Science and leader of the eAgriculture Research Group at Edith Cowan University, Australia. Dr Armstrong is President of the Australian Society of ICT in Agriculture as well as past President of the Asian Federation of Information Technologies in Agriculture (AFITA). She has an international reputation for her research on the use of ICT in agriculture in such areas as agricultural information and decision support systems. Dr Leisa Armstrong is Senior Lecturer in Computer Science and leader of the eAgriculture Research Group at Edith Cowan University, Australia. Dr Armstrong is President of the Australian Society of ICT in Agriculture as well as past President of the Asian Federation of Information Technologies in Agriculture (AFITA). She has an international reputation for her research on the use of ICT in agriculture in such areas as agricultural information and decision support systems. Dr Leisa Armstrong is Senior Lecturer in Computer Science and leader of the eAgriculture Research Group at Edith Cowan University, Australia. Dr Armstrong is President of the Australian Society of ICT in Agriculture as well as past President of the Asian Federation of Information Technologies in Agriculture (AFITA). She has an international reputation for her research on the use of ICT in agriculture in such areas as agricultural information and decision support systems.