We face constant environmental influx, war with pests and pathogens, and fluctuation in markets and risk throughout the farming and agricultural enterprise. Harnessing big data offers a new frontier of knowledge for how and when intervention is essential in agricultural production.
- Where in the field does fertilizer need to be applied? At what rate and time? With what product?
- How do we problem solve and consolidate data?
- When is the best time to harvest based on weather and/or markets?
We then consider how to assess and analyze all available data and quickly make a decision that is actionable by the grower.
Data are being used from the lab to the field, from the scientist to the farmer. For example, plant breeders use data to select preferred genetics and traits, reducing production time of new crops and varieties. Data drive food safety and traceability questions. Real-time data tools are becoming faster and easier to use, with analytics and machine learning adding value across the agricultural enterprise.
Many factors impact agricultural production, like weather, markets, costs of goods and commodity pricing. We have methods to forecast predictions, but those will become more accurate as we improve our ability to integrate diverse data sets to better deliver all perspectives of an issue. This ability, coupled with writing better algorithms and machine learning, leads to improved decision-making in the field.
How We Use Data in the Future
Sensor Data for Precision Agriculture Machinery manufacturers use sensor data to optimize yield and reduce farmers’ costs. An automated and reliable process could measure actual conditions in the field and provide real-time recommendations.
Genotype Modifications and Their Translation to Phenotype and Environmental Responses in Crop Plants Researchers seek to understand the effects of single and multiple genes on plant phenotype to improve the viability of crop plants in the field. Collection and integration of additional data could provide further insight into crop viability.
Relevance to Industry, Producers and Consumers
- Cultivate a workforce proficient in analytics and agriculture
- Develop products that can be applied across diverse crops
- Promote upstream discoveries that benefit various products
Targets to be Addressed
- Data mining and algorithm development for heterogeneous data
- Sensor technology for basic research and field applications
- Heterogeneous data integration for improved breeding and genomic strategies
- Prototyping systems and translating results to crop production and utilization
Connectivity Across Platforms
Recent climate and land use changes continue to significantly impact the environment. We need a historical understanding of plant science to collect data and evaluate how we can have resilient agricultural systems now and in the future. Connecting research in resilient agriculture to predictive analytics ensures we study and create new technologies that will improve plants for years to come.