Making nitrogen (N) fertilizer recommendations is often a guesstimating game, writes Dr. Tai McClellan Maaz, International Plant Nutrition Institute. In an ideal system, we could reliably predict the yield goal for any crop based on environmental conditions. We would then multiply this optimal yield goal by a unit N requirement to calculate the total N needed, assuming no deficiencies in other nutrients, and then determine our rate of fertilizer by deducting residual soil N, in-season mineralized N, and any credits from previous crop or manures. The beauty of this approach is that it is easy to calculate and accounts for N already in the system. It is no wonder that this approach was adopted by many extension programs across the United States. While this method can work well in some areas, such as semi-arid and arid environments, it has proven inaccurate in other areas.
Several sources of uncertainty affect the accuracy of N recommendations. Yield goals are difficult to predict based on data available to us at planting. Estimating plant available N in the system is tough in climates with variable in-season N cycling, movement, and high risk of loss. And even when residual soil N can be reliably measured, or yield goals accurately predicted, we have known for decades that the N requirement for a targeted yield can differ across complex topography or from year to year.
An alternative approach is to amass hundreds of yield response data for a given region and crop rotation to attain a range of optimal N rates, which eliminates the need to soil sample in areas with unpredictable soil N. Recommended rates are thereby based on a large database of N responses which can be updated regularly under a variety of conditions. A large quantity of data also allows users to investigate how certain factors, such as previous crop, change optimal rates. However, the challenge is that yield responses can vary greatly from year to year and within a field, and so this approach is not suited for site-specific management.