Adapting N Recommendations Using Data From Spatial and Temporal Scales Or Different Levels of Adaptive Management.

Traditional nitrogen (N) recommendations are typically based on generalizations from research data and other N response experience.  These general recommendations are often further broken down into categories based on factors such as yield, crop rotation, soil type, management history, etc. While extensive research data and experience serve as the basis for these recommendation categories, they are still usually generalizations.  Thus, these recommendations are somewhat scenario specific, but they are not spatially or temporally specific to a given field, on a given farm, in a given year. While these generalized recommendations are on average right, significant improvements can be made in recommendations beyond these generalized scenario based recommendations, to provide much more accurate, site specific N recommendations.  The N cycle is very dynamic and sensitive to changing environmental conditions, particularly variable weather conditions.  Consequently, temporal data which can capture some of this dynamic behavior of N, such as pre-plant and in-season soil tests, in-season plant tests and sensor readings, has been shown to be very useful in improving N recommendations by better accounting for this temporal variation.   Likewise, N behavior has been shown to exhibit significant spatial variability over relatively short distances.  Spatial data, particularly soil properties like depth, drainage, soil organic matter levels, but also management data such as previous crop patterns, fertilization and manure applications, and direct assessment of crop performance spatially, has also been used to improve N recommendations from field to field and within fields.  It is not realistic, with a dynamic nutrient like N, to expect that we can make consistently accurate N recommendations based on generalizations, even when they have extensive and sound research foundations.  A more realistic goal is to apply all available temporal and spatial data to continually improve N recommendations.  Utilizing all available data and applying the concepts of adaptive management where an N recommendations become an active process not simply an answer that is provided, will lead to constant improvement in N recommendations.