A below-ground agricultural biological performance modeling approach in precision agriculture combines customized field modeling with machine learning techniques for environmental matching of variables to describe a below-surface soil state, to understand and predict the performance of soil-active agricultural biological products such as bio-pesticides, bio-stimulants, plant growth regulators, and other biologically-derives soil adjuvants. The modeling approach characterizes the influence of environmental relationships on the performance of such soil-active agricultural biological products to develop a suite of predictive models to provide notifications, advisories, and recommendations for appropriate products for individual fields.
