As the field of data science and the concept of Big Data intersect with agriculture, growers are challenged to turn jargon and theory into value for their agriculture. Their success depends on reducing risk in the current growing season as well as anticipating pressures faced into the future. As demand for food increases and climate response challenges convention, a grower prepared for this future increases both food production and security. Integrating agricultural data science is critical to this success. Example case studies of current in-practice data science will be discussed. These include the following: 1) daily dispatch and work planning to save labor and fuel costs, 2) using similarity-based climate data analysis to make market decisions, and 3) integrative yield modeling for inventory planning.

Field-level decision making: Daily operation planning through long term agricultural production sustainability

248th National Meeting (2014)

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