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Many public and private decision support systems (DSS) are currently developed. The DSS are defined as computer-based platforms to collect, process, and analyze multi-facet data and generate timely and accurate decisions. Modern DSS includes crop, soil, weather observations, real time machine, sensor, economic and market data, advanced analytics, cloud computing and user-friendly recommendations. Two case studies of public DSS are presented. One is a web-based tool to prescribe variable soybean seeding rates using historical yield maps, yield classification, cost of seed and price of grain. The other web-based platform summarizes yield genotypes by geographies and irrigation management and helps growers to choose the right crop genetics for irrigated or rainfed area. Key barriers to the adoption of modern DSS by farmers and stakeholders are discussed. Future DSS will rely on larger and more diverse datasets, more robust machine learning and process-based models, advanced cloud computing, AI and mobile accessible devices.
- 1 Introduction
- 2 Definition of decision support systems
- 3 Engagement of stakeholders and role of public institutions/industry
- 4 Connectivity, cloud computing
- 5 Data collection and sources
- 6 Internet of Things
- 7 Advanced analytics: modeling, artificial intelligence, machine learning, and deep learning
- 8 Digital twins
- 9 Decision support system case studies
- 10 Main barriers for adoption of decision support systems and future development
- 11 Conclusions
- 12 Where to look for further information
- 13 Acknowledgement
- 14 References